Build an Agentic AI for Literature Analysis

Introduction

In this tutorial, we build a complete scientific discovery agent step by step and experience how each component works together to form a coherent research workflow. We begin by loading our literature corpus, constructing retrieval and LLM modules, and then assembling agents that search papers, generate hypotheses, design experiments, and produce structured reports.

Literature Corpus and Dependencies

import sys, subprocess
 
def install_deps():
   pkgs = ["transformers", "scikit-learn", "numpy"]
   subprocess.check_call([sys.executable, "-m", "pip", "install", "-q"] + pkgs)
 
try:
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np
except ImportError:
   install_deps()
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np
 
np.random.seed(42)
 
LITERATURE = [...]  # Literature Data
 
corpus_texts = [p["abstract"] + " " + p["title"] for p in LITERATURE]
vectorizer = TfidfVectorizer(stop_words="english")
corpus_matrix = vectorizer.fit_transform(corpus_texts)
 
MODEL_NAME = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

We laid the foundation for our scientific agent by loading libraries and preparing the literature corpus. With the model and data structured, we create the computational backbone for everything that follows.

Literature Search Component

@dataclass
class PaperHit:
   paper: Dict[str, Any]
   score: float
 
class LiteratureAgent:
   def __init__(self, vectorizer, corpus_matrix, papers: List[Dict[str, Any]]):
       ...  
 
   def search(self, query: str, k: int = 3) -> List[PaperHit]:
       q_vec = self.vectorizer.transform([query])
       sims = cosine_similarity(q_vec, self.corpus_matrix)[0]
       idxs = np.argsort(-sims)[:k]
       hits = [PaperHit(self.papers[i], float(sims[i])) for i in idxs]
       return hits

We implemented the literature-search component of our agent to identify the most relevant papers using cosine similarity. This gives our system grounding in the closest-matching prior work.

Designing Experiments

@dataclass
class ExperimentPlan:
   ....
 
class ExperimentAgent:
   def design_experiment(self, question: str, hypothesis: str, hits: List[PaperHit]) -> ExperimentPlan:
       ...
 
   def run_experiment(self, plan: ExperimentPlan) -> ExperimentResult:
       base = 0.78 + 0.02 * np.random.randn()
       gain = abs(0.05 + 0.01 * np.random.randn())
       metrics = {"baseline_AUROC": round(base, 3), "augmented_AUROC": round(base + gain, 3)}
       return ExperimentResult(plan=plan, metrics=metrics)

We perform experiments based on the retrieved literature and generated hypotheses, allowing for actionable experimental plans.

Report Generation

class ReportAgent:
   def write_report(self, question: str, hits: List[PaperHit], plan: ExperimentPlan, result: ExperimentResult) -> str:
       ...

We generate a full research-style report using the LLM by assembling the hypothesis, protocol, results, and related work into a structured document.

Conclusion

In conclusion, we see how a compact codebase can evolve into a functioning AI co-researcher capable of searching, reasoning, simulating, and summarizing. Each snippet contributes to the overall pipeline. With a simple yet rich architecture, we can extend functionalities to enhance our scientific exploration.